Develop and optimize machine learning models for various applications.
Preprocess and analyze large datasets to extract meaningful insights.
Deploy ML solutions into production environments using appropriate tools and frameworks.
Collaborate with cross-functional teams to integrate ML models into products and services.
Monitor and evaluate the performance of deployed models.
Requirements
Strong proficiency in Python for data analysis, machine learning, and automation.
Solid understanding of supervised and unsupervised AI/machine learning methods (e.g., XGBoost, LightGBM, Random Forest, clustering, isolation forests, autoencoders, neural networks, transformer-based architectures).
Experience in payment fraud, AML, KYC, or broader risk modeling within fintech or financial institutions.
Experience developing and deploying ML models in production using frameworks such as scikit-learn, TensorFlow, PyTorch, or similar.
Hands-on experience with LLMs (e.g., OpenAI, LLaMA, Claude, Mistral), including use of prompt engineering, retrieval-augmented generation (RAG), and agentic AI to support internal automation and risk workflows.
Ability to work cross-functionally with engineering, product, compliance, and operations teams.
Proven track record of translating complex ML insights into business actions or policy decisions.
Benefits
Flexible work environment
Employee shares options
Health and life insurance
Professional development opportunities
Applicant Tracking System Keywords
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